Abstract: Considering the limitations of wavelet in image representation—that it is only optimal in representing point singularities and difficult to extract curve features of coal and rock images,a new recognition method for low-resolution coal-rock images based on curvelet transform was proposed.The method used curvelet transform to decompose images into curvelet coefficients in different scales.Then,PCA was applied to obtain a lower dimensional representation that was put into a k-NN classifier. Finally,the final recognition result was obtained via weighted fusion of classification results.Experimental results showed that the features extracted by curvelet decomposition could effectively express the curve features of coal-rock images.Compared with several other existing methods,the proposed method had higher recognition accuracy rate,with the average recognition rate reaching 95.0%.Under the condition of low image resolution can it also get high recognition rate and meet the real-time requirements of coal-rock recognition.